Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Fixing Bug Reporting for Mobile and GUI-Based Applications

Smartphones and tablets have established themselves as mainstays in the modern computing landscape. It is conceivable that in the near future such devices may supplant laptops and desktops, becoming many users primary means of carrying out typical computer assisted tasks. In turn, this means that mobile applications will continue on a trajectory to becoming more complex, and the primary focus of millions of developers worldwide. In order to properly create and maintain these apps developers will need support, especially with regard to the prompt confirmation and resolution of bug reports. Unfortunately, current issue tracking systems typically only implement collection of coarse grained natural language descriptions, and lack features to facilitate reporters including important information in their reports. This illustrates the lexical information gap that exists in current bug reporting systems for mobile and GUI-based apps. This paper outlines promising preliminary work towards addressing this problem and proposes a comprehensive research program which aims to implement new bug reporting mechanisms and examine the impact that they might have on related software maintenance tasks

FUSION: A Tool for Facilitating and Augmenting Android Bug Reporting

As the popularity of mobile smart devices continues to climb the complexity of apps continues to increase, making the development and maintenance process challenging. Current bug tracking systems lack key features to effectively support construction of reports with actionable information that directly lead to a bug\u27s resolution. In this demo we present the implementation of a novel bug reporting system, called FUSION, that facilitates users including reproduction steps in bug reports for mobile apps. FUSION links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. Results of preliminary studies demonstrate that FUSION both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information. Tool website: www.fusion-android.com Video url: https://youtu.be/AND9h0E1xR